Featured PGRN Investigators

​I joined the Department of Clinical Pharmacy in the University of Michigan College of Pharmacy as a research assistant professor in 2013 and became an assistant professor in 2016. I received my PharmD from the Ernest Mario School of Pharmacy at Rutgers University and PhD in pharmaceutical sciences from the University of North Carolina Eshelman School of Pharmacy working with Dr. Howard McLeod in cancer pharmacogenetics, specifically the pharmacogenetics of taxane-induced neuropathy. My research focus is the discovery and translation of patient-specific (clinical, genetic, kinetic, metabolomic, etc.) predictors of cancer treatment outcomes. My career objective is to develop tools for individualized cancer treatment and translate them into clinical practice to optimize therapeutic outcomes. Within my overall career objective, since graduate school, I have been particularly interested in prediction and avoidance of taxane-induced peripheral neuropathy.

I have performed several retrospective analyses of clinical trial datasets to discover pharmacogenetic, pharmacokinetic, and pharmacometabolomic predictors of paclitaxel-induced peripheral neuropathy and am currently collecting and analyzing larger cooperative group clinical trial cohorts to validate these findings. One ongoing project that I am particularly excited is using our previously published neuropathy prediction model, which includes paclitaxel pharmacokinetics and dosing, and attempting to introduce genetics to explain residual variability in the “neuropathy sensitivity” phenotype. I also have ongoing work in the pharmacogenetics of hormonal treatment in breast cancer. I am currently leading a meta-analysis of all patient cohorts that have tamoxifen pharmacokinetic data and CYP2D6 genetic data to generate an endoxifen prediction algorithm and empirically derive estimates of the percentage activity of individual CYP2D6 alleles. I hope that this project will improve the accuracy of translating CYP2D6 genotype into predicted activity phenotype and produce a percentage phenotypic activity that is more intuitive for clinicians.​​